Rational Design of Covalent Kinase Inhibitors by an Integrated Computational Workflow (Kin-Cov)

Area of Science:

• Medicinal Chemistry
• Computational Drug Design
• Biochemistry

Background:

• Covalent kinase inhibitors (CKIs) offer significant therapeutic potential but their rational design is challenging.
• Computational approaches for designing CKIs are currently limited.

Purpose of the Study:

• To present an integrated computational workflow (Kin-Cov) for the rational design of CKIs.
• To demonstrate the workflow's utility by designing the first covalent inhibitor for leucine-zipper and sterile-α motif kinase (ZAK).

Main Methods:

• Development of the Kin-Cov computational workflow for CKI design.
• Application of the workflow to target ZAK kinase.
• Synthesis and biochemical evaluation of designed compounds.
• Kinome profiling for target specificity assessment.
• Structural biology and cell-based assays to confirm irreversible binding.

Main Results:

• The Kin-Cov workflow successfully designed novel covalent ZAK kinase inhibitors.
• Two representative compounds, 7 and 8, exhibited potent inhibition of ZAK kinase with IC50 values of 9.1 nM and 11.5 nM, respectively.
• Compound 8 demonstrated high specificity against 378 wild-type kinases.
• Irreversible binding was confirmed through structural and cellular assays.

Conclusions:

• The Kin-Cov workflow provides a rational and generalizable approach for designing CKIs.
• This strategy leverages kinase nucleophilic residue reactivity and accessibility for targeted inhibitor development.
• The workflow can accelerate the discovery of novel CKI-based therapeutics.